Abstract

Nanopores represent the first commercial technology in decades to present a significantly different technique for DNA sequencing, and one of the first technologies to propose direct RNA sequencing. Despite significant differences with previous sequencing technologies, read simulators to date make similar assumptions with respect to error profiles and their analysis, resulting in incorrect characterization of nanopore error. This is a great disservice to both nanopore sequencing and to computer scientists who seek to optimize their tools for the platform. Previous works have discussed the occurrence of some bias in the identifiability of certain k-mers, but this discussion has been focused on homopolymers, leaving unanswered the question of whether k-mer bias exists over all k-mers, the strength of the bias, how it occurs, and what can be done to reduce the effects. In this work, we demonstrate that current read simulators fail to accurately represent k-mer error distributions. We explore the sources of k-mer bias in nanopore basecalls, and we present a model for predicting k-mers that are difficult to identify. We also propose SNaReSim, a new state-of-the-art simulator, and demonstrate that it provides higher accuracy with respect to 6- mer accuracy biases.

abstract = "Nanopores represent the first commercial technology in decades to present a significantly different technique for DNA sequencing, and one of the first technologies to propose direct RNA sequencing. Despite significant differences with previous sequencing technologies, read simulators to date make similar assumptions with respect to error profiles and their analysis, resulting in incorrect characterization of nanopore error. This is a great disservice to both nanopore sequencing and to computer scientists who seek to optimize their tools for the platform. Previous works have discussed the occurrence of some bias in the identifiability of certain k-mers, but this discussion has been focused on homopolymers, leaving unanswered the question of whether k-mer bias exists over all k-mers, the strength of the bias, how it occurs, and what can be done to reduce the effects. In this work, we demonstrate that current read simulators fail to accurately represent k-mer error distributions. We explore the sources of k-mer bias in nanopore basecalls, and we present a model for predicting k-mers that are difficult to identify. We also propose SNaReSim, a new state-of-the-art simulator, and demonstrate that it provides higher accuracy with respect to 6- mer accuracy biases.",

N2 - Nanopores represent the first commercial technology in decades to present a significantly different technique for DNA sequencing, and one of the first technologies to propose direct RNA sequencing. Despite significant differences with previous sequencing technologies, read simulators to date make similar assumptions with respect to error profiles and their analysis, resulting in incorrect characterization of nanopore error. This is a great disservice to both nanopore sequencing and to computer scientists who seek to optimize their tools for the platform. Previous works have discussed the occurrence of some bias in the identifiability of certain k-mers, but this discussion has been focused on homopolymers, leaving unanswered the question of whether k-mer bias exists over all k-mers, the strength of the bias, how it occurs, and what can be done to reduce the effects. In this work, we demonstrate that current read simulators fail to accurately represent k-mer error distributions. We explore the sources of k-mer bias in nanopore basecalls, and we present a model for predicting k-mers that are difficult to identify. We also propose SNaReSim, a new state-of-the-art simulator, and demonstrate that it provides higher accuracy with respect to 6- mer accuracy biases.

AB - Nanopores represent the first commercial technology in decades to present a significantly different technique for DNA sequencing, and one of the first technologies to propose direct RNA sequencing. Despite significant differences with previous sequencing technologies, read simulators to date make similar assumptions with respect to error profiles and their analysis, resulting in incorrect characterization of nanopore error. This is a great disservice to both nanopore sequencing and to computer scientists who seek to optimize their tools for the platform. Previous works have discussed the occurrence of some bias in the identifiability of certain k-mers, but this discussion has been focused on homopolymers, leaving unanswered the question of whether k-mer bias exists over all k-mers, the strength of the bias, how it occurs, and what can be done to reduce the effects. In this work, we demonstrate that current read simulators fail to accurately represent k-mer error distributions. We explore the sources of k-mer bias in nanopore basecalls, and we present a model for predicting k-mers that are difficult to identify. We also propose SNaReSim, a new state-of-the-art simulator, and demonstrate that it provides higher accuracy with respect to 6- mer accuracy biases.